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Agritourism is an expanding form of experience-based rural tourism, yet limited empirical research explains how experiential marketing shapes perceived value and satisfaction in authentic farming contexts. Drawing on Schmitt's Strategic Experiential Modules and the Memorable Tourism Experience (MTE) framework, this study develops and tests a structural model linking agritourism experience, perceived value, and satisfaction. Survey data from 398 visitors across twelve certified agritourist communities in Taiwan were analyzed using CFA and SEM. Results show that agritourism experiences significantly enhance perceived value and directly increase satisfaction, with perceived value exerting a strong mediating effect. From a sustainability perspective, the findings underscore the distinctiveness of agritourism, where authenticity, natural variability, and human-land interactions generate experiential outcomes not replicable in constructed tourism spaces. The study advances experiential marketing theory and offers practical guidance for rural tourism development, thereby supporting sustainable rural development by fostering long-term tourist engagement and local economic vitality.
As artificial intelligence and machine learning advance, increasing privacy concerns and regulatory constraints have limited cross-border data sharing for traditional model training. Federated Learning (FL) offers a privacy-preserving approach by enabling distributed training without exposing raw data. However, FL faces significant challenges, particularly when dealing with Non-Independent and Identically Distributed (Non-IID) data, which results in inconsistent model performance. Moreover, relying on a central server also raises reliability and scalability issues. Decentralized Federated Learning (DFL) eliminates the central server, thereby fostering more robust and scalable collaboration. Despite the growing interest in DFL, a comprehensive review focusing on Non-IID challenges remains scarce. This article presents a Systematic Literature Review (SLR) of existing research on DFL under Non-IID settings. Studies were retrieved from six major academic publishers and categorized into four pillars: architecture, topology, optimization, and security. The SLR review provides insights into current trends and systematically summarizes real-world applications, commonly used datasets, and neural network models. This article also examines prevalent methods for conducting Non-IID experiments and evaluating performance metrics. By providing a structured analysis of the literature, experimental setups, and evaluation practices, this survey highlights key trends, uncovers research gaps, and proposes future directions for advancing DFL in Non-IID environments.
Oxidative phosphorylation (OXPHOS) and mitophagy are functionally interconnected cellular processes, the defects of which are considered key driving forces behind the pathogenesis of Parkinson’s disease (PD). UQCRC1, a core subunit of the mitochondrial respiratory chain complex III, is a recently identified familial PD gene whose pathogenic mutations result in OXPHOS stress. Given its importance, however, the role of UQCRC1 in idiopathic PD as well as mitophagy has not been investigated. In this study, we collected 19 datasets comprising postmortem substantia nigra from 150 cases of non-disease controls and 185 cases of PD or incidental Lewy body disease (iLBD), and the meta-analysis of the UQCRC1 mRNA level showed reduced expression in idiopathic PD, suggesting the potential of UQCRC1 as a biomarker. Leveraging the SH-SY5Y cells and fly models, we showed that mitophagy was impaired upon UQCRC1 mutation or depletion. Notably, insufficiency of PINK1 mRNA was associated with UQCRC1 deficiency, and overexpression of Pink1 rescued the locomotion and mitophagy defects in the fly models with neuronal loss of uqcrc1. Treatment with two PINK1 activators, kinetin and MTK458, resulted in similar protective effects in the fly and cell models. Overall, we identified OXPHOS stress led by deficiency of UQCRC1 as an etiology of mitophagy defects in PD and PINK1 as a therapeutic target for UQCRC1-associated PD.
A cholesteric liquid crystal (CLC) system comprising the calamitic LC 8CB (4-octyl-4’-cyanobiphenyl) doped with the chiral agent R5011 was applied as a biosensing medium. By exploiting the pronounced light-scattering behavior at the smectic-to-chiral nematic transition temperature of 26.5 °C of the vertically aligned CLC, a distinct red–green (signal–background) color contrast was observed in the optical texture of the CLC in the presence of the protein standard bovine serum albumin (BSA) or the cancer biomarker CA125, with the red color intensity correlated positively with the concentration of the biological analyte. The high-contrast signal–background color scheme of the 8CB/R5011 CLC was confirmed through theoretical calculations to simulate the interference spectra and the corresponding color observed under a polarizing optical microscope. The simulation results also indicate that the collective tilt angle of the CLC increased in the presence of biomolecules, suggesting that the alignment of CLC was disrupted and light scattering was enhanced. By incorporating haze measurements as the quantitative approach for the CLC-based biosensor, a strong correlation between the mean haze value and the analyte concentration was demonstrated. The limit of detection (LOD) achieved through haze analysis was 1.23 × 10⁻¹² g·mL⁻1 for BSA and 3.85 × 10⁻¹⁰ g·mL⁻1 for CA125, which were substantially lower than those obtained via conventional image analysis (2.21 × 10⁻¹¹-g·mL⁻1 BSA and 1.43 × 10⁻⁹-g·mL⁻1 CA125). In a proof-of-concept demonstration with CA125-spiked human serum as the analyte, the LOD remained unaffected by interferents present in human serum. The CLC-based biosensing technology combined with haze-based quantitation offers a label-free, highly sensitive, rapid response, cost-effective, and versatile platform for detecting biological analytes, manifesting significant potential for applications in early disease diagnosis and biomedical research.
The Surface Water and Ocean Topography mission (SWOT), equipped with the Ka-band Radar Interferometer (KaRIn), provides groundbreaking 2-D sea surface heights (SSHs), bringing new potential for optimizing the deflection of the vertical (DOVs). However, conventional DOV modeling-combining along- and cross-track geoid gradients with equal weights-fail to fully exploit the potential of SWOT/KaRIn observations and overlook the spatial variability in precision. We present a tailored method for optimizing DOVs estimation. The method combines geoid gradients in the along-track, cross-track, diagonal (forward and backward) directions with adaptive weighting. The refined weights are employed to exploit the potential of each geoid gradient based on the relationship between the standard deviation of SSHs and the significant wave height. To mitigate data gaps, prior and locally averaged geoid gradients are incorporated in the gaps and overlapping regions. SWOT/KaRIn-derived DOVs and gravity anomalies from the science-phase observations are validated against shipborne gravity in the Philippine Sea. Results indicate that the DOV model derived by the tailored method-particularly by combining triple-directional (along, cross and diagonally forward) geoid gradients with refined weights-achieves a 7.3 percent improvement in accuracy over the conventional method. The supplement of additional geoid gradients is critical for mitigating leakage errors caused by missing or reduced observations in the gap regions. Furthermore, the gravity anomaly model recovered from DOVs by stacking 17-cycle observations achieved an accuracy of 2.97 mGal, representing a 7.2 percent improvement over single-cycle observations. The clear advantages of SWOT/KaRIn observations are gradually emerging in marine gravity recovery.
